
"""simple docstring"""



import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def 			_UpperCamelCase						(    _A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = checkpoints.load_tax_checkpoint(_A   )
    _UpperCAmelCase        = flatten_dict(_A   )
    return flax_params


def 			_UpperCamelCase						(    _A   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = {}

    _UpperCAmelCase        = {
        """token_embedder""": """embeddings""",
        """encoder_norm""": """layernorm""",
        """kernel""": """weight""",
        """.out""": """.output""",
        """scale""": """weight""",
        """embedders_0.pos_embedding""": """row_embedder.weight""",
        """embedders_1.pos_embedding""": """column_embedder.weight""",
    }

    _UpperCAmelCase        = {
        """query""": """attention.query""",
        """key""": """attention.key""",
        """value""": """attention.value""",
        """output.dense""": """output""",
        """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
        """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
        """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
        """mlp.""": """mlp.DenseReluDense.""",
        """pre_mlp_layer_norm""": """mlp.layer_norm""",
        """self_attention.o""": """self_attention.attention.o""",
        """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
        """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
        """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            _UpperCAmelCase        = """.""".join(key[1:]   )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                _UpperCAmelCase        = new_key.replace(_A       ,   _A   )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    _UpperCAmelCase        = new_key.replace(_A       ,   _A   )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                _UpperCAmelCase        = re.sub(R"""layers_(\d+)"""       ,   R"""layer.\1"""       ,   _A   )
                _UpperCAmelCase        = new_key.replace("""encoder"""       ,   """encoder.encoder"""   )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                _UpperCAmelCase        = re.sub(R"""layers_(\d+)"""       ,   R"""layer.\1"""       ,   _A   )

            _UpperCAmelCase        = flax_dict[key]

    _UpperCAmelCase        = {}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            _UpperCAmelCase        = torch.from_numpy(converted_dict[key].T   )
        else:
            _UpperCAmelCase        = torch.from_numpy(converted_dict[key]   )

    return converted_torch_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=False       ,   _A=False   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_flax_param(_A   )

    if not use_large:
        _UpperCAmelCase        = PixaStructVisionConfig()
        _UpperCAmelCase        = PixaStructTextConfig()
    else:
        _UpperCAmelCase        = PixaStructVisionConfig(
            hidden_size=1_5_3_6       ,   d_ff=3_9_6_8       ,   num_attention_heads=2_4       ,   num_hidden_layers=1_8   )
        _UpperCAmelCase        = PixaStructTextConfig(hidden_size=1_5_3_6       ,   d_ff=3_9_6_8       ,   num_heads=2_4       ,   num_layers=1_8   )
    _UpperCAmelCase        = PixaStructConfig(
        vision_config=encoder_config.to_dict()       ,   text_config=decoder_config.to_dict()       ,   is_vqa=_A   )

    _UpperCAmelCase        = PixaStructForConditionalGeneration(_A   )

    _UpperCAmelCase        = rename_and_convert_flax_params(_A   )
    model.load_state_dict(_A   )

    _UpperCAmelCase        = AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""   )
    _UpperCAmelCase        = PixaStructImageProcessor()
    _UpperCAmelCase        = PixaStructProcessor(image_processor=_A       ,   tokenizer=_A   )

    if use_large:
        _UpperCAmelCase        = 4_0_9_6

    _UpperCAmelCase        = True

    # mkdir if needed
    os.makedirs(_A       ,   exist_ok=_A   )

    model.save_pretrained(_A   )
    processor.save_pretrained(_A   )

    print("""Model saved in {}""".format(_A   )   )


if __name__ == "__main__":
 a       :						int       		=		argparse.ArgumentParser()
 parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
 parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
 a       :						Dict       		=		parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )

"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step